Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:29:25.637071
Analysis finished2020-08-25 00:29:46.859421
Duration21.22 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.963979869491823e-11
Minimum-2.202409267425537
Maximum2.244232416152954
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:46.905816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.202409267
5-th percentile-1.584626591
Q1-0.7607555836
median-0.02201686334
Q30.7440897822
95-th percentile1.670290881
Maximum2.244232416
Range4.446641684
Interquartile range (IQR)1.504845366

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-1.11557591e+10
Kurtosis-0.8091421213
Mean-8.963979869e-11
Median Absolute Deviation (MAD)0.7590702474
Skewness0.09057283272
Sum-8.963979869e-08
Variance1
2020-08-25T00:29:47.008334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.738279342710.1%
 
-0.0279950220110.1%
 
-0.24137866510.1%
 
0.453459918510.1%
 
-0.524106442910.1%
 
-1.29624259510.1%
 
-1.41148638710.1%
 
2.23703622810.1%
 
1.03904724110.1%
 
-0.491541057810.1%
 
-0.701827764510.1%
 
0.615885913410.1%
 
0.125162392910.1%
 
-0.730446457910.1%
 
0.451825976410.1%
 
1.59503376510.1%
 
-0.234131172310.1%
 
-1.02339625410.1%
 
-0.546998918110.1%
 
-0.536351859610.1%
 
-0.208166241610.1%
 
-1.55595350310.1%
 
-0.10798896110.1%
 
-0.740864574910.1%
 
-1.63407015810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.20240926710.1%
 
-2.18814802210.1%
 
-2.10233902910.1%
 
-2.09406089810.1%
 
-2.07882714310.1%
 
-2.06926798810.1%
 
-2.04419279110.1%
 
-2.02503061310.1%
 
-1.97400939510.1%
 
-1.9063525210.1%
 
ValueCountFrequency (%) 
2.24423241610.1%
 
2.23703622810.1%
 
2.23454809210.1%
 
2.20662093210.1%
 
2.17429447210.1%
 
2.14507341410.1%
 
2.12766075110.1%
 
2.12226128610.1%
 
2.11244106310.1%
 
2.1038932810.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.6810981631279e-10
Minimum-1.6999146938323977
Maximum1.7985024452209473
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:47.128033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.699914694
5-th percentile-1.507673705
Q1-0.8672545254
median-0.02687574085
Q30.8681760877
95-th percentile1.620564044
Maximum1.798502445
Range3.498417139
Interquartile range (IQR)1.735430613

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-1032940668
Kurtosis-1.173388632
Mean-9.681098163e-10
Median Absolute Deviation (MAD)0.8634471828
Skewness0.09435106119
Sum-9.681098163e-07
Variance1.000000001
2020-08-25T00:29:47.233276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.451170623310.1%
 
-0.459936708210.1%
 
1.45835387710.1%
 
0.897144198410.1%
 
-0.952466905110.1%
 
0.97917234910.1%
 
-1.48958325410.1%
 
-0.526045262810.1%
 
-1.51302695310.1%
 
-1.11067640810.1%
 
-0.559244692310.1%
 
-0.13785776510.1%
 
0.407550692610.1%
 
1.30207610110.1%
 
1.02082252510.1%
 
-0.983732521510.1%
 
-1.27465689210.1%
 
-1.50127923510.1%
 
-1.59893095510.1%
 
-1.55204844510.1%
 
1.04423284510.1%
 
1.7590744510.1%
 
1.58982574910.1%
 
0.7076569210.1%
 
1.41140139110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.69991469410.1%
 
-1.69974541710.1%
 
-1.69434583210.1%
 
-1.6917364610.1%
 
-1.68685162110.1%
 
-1.68390393310.1%
 
-1.66359019310.1%
 
-1.65735340110.1%
 
-1.65051865610.1%
 
-1.64707648810.1%
 
ValueCountFrequency (%) 
1.79850244510.1%
 
1.79649698710.1%
 
1.78368234610.1%
 
1.7821831710.1%
 
1.77665293210.1%
 
1.77546632310.1%
 
1.76753294510.1%
 
1.76671874510.1%
 
1.76637268110.1%
 
1.7590744510.1%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0825460767804884e-09
Minimum-2.160346984863281
Maximum3.7268602848052983
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:47.350592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.160346985
5-th percentile-1.303818762
Q1-0.7514732331
median-0.1839373335
Q30.6014242619
95-th percentile1.884221327
Maximum3.726860285
Range5.88720727
Interquartile range (IQR)1.352897495

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)923748209.4
Kurtosis0.2466847999
Mean1.082546077e-09
Median Absolute Deviation (MAD)0.633522585
Skewness0.7414630375
Sum1.082546077e-06
Variance1
2020-08-25T00:29:47.466393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.626952350110.1%
 
1.71358394610.1%
 
0.971361875510.1%
 
-0.00615441845710.1%
 
-0.346030771710.1%
 
-1.82551908510.1%
 
1.33723461610.1%
 
-0.305011361810.1%
 
-0.93294727810.1%
 
0.00822967663410.1%
 
1.88409292710.1%
 
0.82445514210.1%
 
0.186194434810.1%
 
-0.214026212710.1%
 
0.162267953210.1%
 
-0.883871376510.1%
 
-0.789654433710.1%
 
-1.25515377510.1%
 
-1.32546079210.1%
 
0.0330577865210.1%
 
-0.638428628410.1%
 
0.497740656110.1%
 
0.236480727810.1%
 
0.664671063410.1%
 
-1.33324456210.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.16034698510.1%
 
-2.05520844510.1%
 
-2.04262757310.1%
 
-1.94873189910.1%
 
-1.9224357610.1%
 
-1.86435306110.1%
 
-1.82551908510.1%
 
-1.8030848510.1%
 
-1.79873943310.1%
 
-1.74928462510.1%
 
ValueCountFrequency (%) 
3.72686028510.1%
 
3.57192254110.1%
 
3.21629810310.1%
 
3.05693101910.1%
 
3.012250910.1%
 
2.98292779910.1%
 
2.98132634210.1%
 
2.9614696510.1%
 
2.91255021110.1%
 
2.89602208110.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9185245037078858e-09
Minimum-1.7148575782775881
Maximum1.787419676780701
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:47.581654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.714857578
5-th percentile-1.559788615
Q1-0.8585219979
median-0.04061810486
Q30.8424962312
95-th percentile1.60050925
Maximum1.787419677
Range3.502277255
Interquartile range (IQR)1.701018229

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)521233895.5
Kurtosis-1.164821164
Mean1.918524504e-09
Median Absolute Deviation (MAD)0.8409873247
Skewness0.06738549629
Sum1.918524504e-06
Variance1.000000001
2020-08-25T00:29:47.847815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.459960192410.1%
 
0.423187464510.1%
 
-1.21624505510.1%
 
-0.204764127710.1%
 
-0.498395383410.1%
 
1.56388735810.1%
 
-0.256489753710.1%
 
0.762410640710.1%
 
0.192066311810.1%
 
1.45059013410.1%
 
-0.462735831710.1%
 
-0.997928619410.1%
 
1.36424696410.1%
 
0.235520437410.1%
 
0.126999616610.1%
 
0.770201325410.1%
 
-0.489581227310.1%
 
0.545590043110.1%
 
-0.183271944510.1%
 
0.330410808310.1%
 
1.58726584910.1%
 
1.46226465710.1%
 
-1.56772959210.1%
 
1.20444655410.1%
 
0.731128573410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.71485757810.1%
 
-1.71476793310.1%
 
-1.71417212510.1%
 
-1.70301449310.1%
 
-1.70031285310.1%
 
-1.69794571410.1%
 
-1.69056618210.1%
 
-1.68735051210.1%
 
-1.68730616610.1%
 
-1.68599474410.1%
 
ValueCountFrequency (%) 
1.78741967710.1%
 
1.77742624310.1%
 
1.7752194410.1%
 
1.77362012910.1%
 
1.7641836410.1%
 
1.76039195110.1%
 
1.7584246410.1%
 
1.75812888110.1%
 
1.74954903110.1%
 
1.74540650810.1%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.834751158952714e-10
Minimum-1.702661633491516
Maximum1.7385513782501218
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:47.963150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.702661633
5-th percentile-1.533249533
Q1-0.8597988933
median-0.002441011486
Q30.8638215661
95-th percentile1.557474494
Maximum1.738551378
Range3.441213012
Interquartile range (IQR)1.723620459

Descriptive statistics

Standard deviation0.9999999989
Coefficient of variation (CV)-1276364723
Kurtosis-1.196272538
Mean-7.834751159e-10
Median Absolute Deviation (MAD)0.858472295
Skewness0.02409846968
Sum-7.834751159e-07
Variance0.9999999978
2020-08-25T00:29:48.065794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.890623450310.1%
 
1.45836377110.1%
 
-0.344874620410.1%
 
-1.02482032810.1%
 
-0.963581800510.1%
 
0.463234275610.1%
 
1.66934168310.1%
 
-0.547558963310.1%
 
-0.992870509610.1%
 
0.682319223910.1%
 
1.31240868610.1%
 
-1.64197468810.1%
 
-1.33459436910.1%
 
-0.126610398310.1%
 
0.458651006210.1%
 
-1.39195501810.1%
 
-0.112140379810.1%
 
1.68372750310.1%
 
-0.580741047910.1%
 
-1.32554435710.1%
 
-0.923168063210.1%
 
-0.339197099210.1%
 
-0.639330148710.1%
 
-1.51202881310.1%
 
0.945584416410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.70266163310.1%
 
-1.69908666610.1%
 
-1.69868302310.1%
 
-1.6918147810.1%
 
-1.68705034310.1%
 
-1.68591809310.1%
 
-1.68130624310.1%
 
-1.67284059510.1%
 
-1.66762137410.1%
 
-1.66443300210.1%
 
ValueCountFrequency (%) 
1.73855137810.1%
 
1.73756051110.1%
 
1.73152661310.1%
 
1.72388923210.1%
 
1.72199559210.1%
 
1.72089660210.1%
 
1.71971023110.1%
 
1.71512281910.1%
 
1.71452939510.1%
 
1.71216678610.1%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.525094017386437e-10
Minimum-1.6706713438034058
Maximum1.7377748489379885
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:48.180681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.670671344
5-th percentile-1.51437422
Q1-0.8875145912
median-0.05978830531
Q30.8778575361
95-th percentile1.59118275
Maximum1.737774849
Range3.408446193
Interquartile range (IQR)1.765372127

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-1173007592
Kurtosis-1.217076743
Mean-8.525094017e-10
Median Absolute Deviation (MAD)0.8807156086
Skewness0.1060285369
Sum-8.525094017e-07
Variance1
2020-08-25T00:29:48.285933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.0201157238310.1%
 
-0.261039495510.1%
 
1.34901404410.1%
 
1.36986911310.1%
 
0.341157674810.1%
 
-0.828792750810.1%
 
-1.12242376810.1%
 
-0.714819788910.1%
 
-0.794653892510.1%
 
0.898620009410.1%
 
-1.53255701110.1%
 
0.651043415110.1%
 
-1.5833338510.1%
 
0.64127403510.1%
 
-0.361650824510.1%
 
-0.102619580910.1%
 
-1.1008176810.1%
 
0.00479626003710.1%
 
-1.38922691310.1%
 
-0.174157097910.1%
 
0.179846614610.1%
 
0.152043521410.1%
 
0.873679459110.1%
 
0.240176603210.1%
 
0.611959099810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.67067134410.1%
 
-1.66955614110.1%
 
-1.66846728310.1%
 
-1.66547167310.1%
 
-1.66496634510.1%
 
-1.65049219110.1%
 
-1.64736652410.1%
 
-1.64709699210.1%
 
-1.6462348710.1%
 
-1.63992536110.1%
 
ValueCountFrequency (%) 
1.73777484910.1%
 
1.73513901210.1%
 
1.73468494410.1%
 
1.73109412210.1%
 
1.72575175810.1%
 
1.72449064310.1%
 
1.72405040310.1%
 
1.72209978110.1%
 
1.72192382810.1%
 
1.72187006510.1%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.9814040064811706e-10
Minimum-1.7702804803848269
Maximum1.6774529218673706
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:48.401035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.77028048
5-th percentile-1.60244832
Q1-0.8752927184
median0.02694592159
Q30.8935836405
95-th percentile1.502601105
Maximum1.677452922
Range3.447733402
Interquartile range (IQR)1.768876359

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-2511676781
Kurtosis-1.215058684
Mean-3.981404006e-10
Median Absolute Deviation (MAD)0.8862418234
Skewness-0.06383271031
Sum-3.981404006e-07
Variance1
2020-08-25T00:29:48.502348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.01171624710.1%
 
-1.16288244710.1%
 
-1.4986220610.1%
 
1.01699793310.1%
 
0.431983709310.1%
 
1.30605125410.1%
 
-0.631540298510.1%
 
0.360690385110.1%
 
-0.850780606310.1%
 
0.832706868610.1%
 
-0.602228581910.1%
 
-0.707354664810.1%
 
-0.11019039910.1%
 
-0.824879765510.1%
 
-0.871326446510.1%
 
1.46840584310.1%
 
-0.552541732810.1%
 
-0.325520932710.1%
 
0.711766481410.1%
 
-1.56241273910.1%
 
-0.518226265910.1%
 
-1.67529594910.1%
 
1.1056375510.1%
 
-0.789703726810.1%
 
0.662743866410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.7702804810.1%
 
-1.76558601910.1%
 
-1.76549100910.1%
 
-1.75481224110.1%
 
-1.75383198310.1%
 
-1.74937665510.1%
 
-1.74814307710.1%
 
-1.748058210.1%
 
-1.74540901210.1%
 
-1.73016083210.1%
 
ValueCountFrequency (%) 
1.67745292210.1%
 
1.6761611710.1%
 
1.67105698610.1%
 
1.66942834910.1%
 
1.66444146610.1%
 
1.66313278710.1%
 
1.65977823710.1%
 
1.65908634710.1%
 
1.659045110.1%
 
1.65494680410.1%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6542617231607438e-09
Minimum-1.7239429950714111
Maximum1.6978634595870972
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:48.624221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.723942995
5-th percentile-1.567904246
Q1-0.855770722
median-0.02160639316
Q30.8875822425
95-th percentile1.570598102
Maximum1.69786346
Range3.421806455
Interquartile range (IQR)1.743352965

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)604499267.5
Kurtosis-1.166384888
Mean1.654261723e-09
Median Absolute Deviation (MAD)0.8647813499
Skewness0.01735871475
Sum1.654261723e-06
Variance0.9999999999
2020-08-25T00:29:48.729613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.28124046310.1%
 
1.54038274310.1%
 
-0.0250457562510.1%
 
-0.177905231710.1%
 
-1.19276988510.1%
 
-1.52776801610.1%
 
-0.938176333910.1%
 
-0.61004936710.1%
 
0.462260425110.1%
 
-0.179367065410.1%
 
-0.932311356110.1%
 
0.146904081110.1%
 
1.04039895510.1%
 
-0.899100899710.1%
 
1.26304137710.1%
 
0.369999080910.1%
 
-0.244509279710.1%
 
1.57162368310.1%
 
0.83659255510.1%
 
-1.56779277310.1%
 
0.706391930610.1%
 
0.0821126252410.1%
 
0.133951276510.1%
 
-1.16145300910.1%
 
-1.12041294610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.72394299510.1%
 
-1.72130048310.1%
 
-1.72079002910.1%
 
-1.71798253110.1%
 
-1.71672284610.1%
 
-1.71455705210.1%
 
-1.71019232310.1%
 
-1.70240366510.1%
 
-1.70222449310.1%
 
-1.70075166210.1%
 
ValueCountFrequency (%) 
1.6978634610.1%
 
1.69700503310.1%
 
1.69486665710.1%
 
1.69377481910.1%
 
1.69204604610.1%
 
1.68256139810.1%
 
1.68205463910.1%
 
1.6768969310.1%
 
1.67671024810.1%
 
1.67183017710.1%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.890025593340397e-10
Minimum-1.806440114974976
Maximum1.693694829940796
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:48.845658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.806440115
5-th percentile-1.596701896
Q1-0.8650304228
median0.04197281972
Q30.8669193685
95-th percentile1.56149826
Maximum1.69369483
Range3.500134945
Interquartile range (IQR)1.731949791

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)-2044979076
Kurtosis-1.185242365
Mean-4.890025593e-10
Median Absolute Deviation (MAD)0.8628925979
Skewness-0.07208916384
Sum-4.890025593e-07
Variance1.000000004
2020-08-25T00:29:48.953751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.69140887310.1%
 
1.69266402710.1%
 
1.51941585510.1%
 
1.32820010210.1%
 
-0.334304809610.1%
 
0.225386023510.1%
 
-1.69659209310.1%
 
-1.34893393510.1%
 
-1.46402037110.1%
 
0.822900772110.1%
 
0.0987121760810.1%
 
1.20095658310.1%
 
1.60081517710.1%
 
0.975726664110.1%
 
0.488596707610.1%
 
-0.431954860710.1%
 
-1.11837232110.1%
 
0.412422388810.1%
 
0.121690742710.1%
 
-1.7761075510.1%
 
-0.44171652210.1%
 
-0.127551436410.1%
 
-0.0310251619710.1%
 
-1.13793921510.1%
 
1.36449897310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.80644011510.1%
 
-1.80546414910.1%
 
-1.80051195610.1%
 
-1.79232442410.1%
 
-1.77905702610.1%
 
-1.77739262610.1%
 
-1.7761075510.1%
 
-1.77019679510.1%
 
-1.75424969210.1%
 
-1.74389791510.1%
 
ValueCountFrequency (%) 
1.6936948310.1%
 
1.69361686710.1%
 
1.69343030510.1%
 
1.69266402710.1%
 
1.6819474710.1%
 
1.68165552610.1%
 
1.67946481710.1%
 
1.67923378910.1%
 
1.67306959610.1%
 
1.67219340810.1%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.597276423126459e-09
Minimum-1.8100426197052
Maximum1.7054005861282349
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:49.071414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.81004262
5-th percentile-1.631662685
Q1-0.801892668
median0.01713785902
Q30.8483417183
95-th percentile1.581701458
Maximum1.705400586
Range3.515443206
Interquartile range (IQR)1.650234386

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-626065711.8
Kurtosis-1.095438487
Mean-1.597276423e-09
Median Absolute Deviation (MAD)0.8242694438
Skewness-0.04487232255
Sum-1.597276423e-06
Variance1.000000002
2020-08-25T00:29:49.175713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.187988147110.1%
 
-1.48309004310.1%
 
0.170577138710.1%
 
0.598942220210.1%
 
-1.14586436710.1%
 
-1.35289263710.1%
 
-0.617851972610.1%
 
0.0467151142710.1%
 
1.22007536910.1%
 
0.1935583810.1%
 
1.18881106410.1%
 
-1.7513086810.1%
 
0.694007098710.1%
 
-0.350909441710.1%
 
-0.302079856410.1%
 
-1.50128340710.1%
 
0.938352525210.1%
 
1.22002565910.1%
 
0.80337059510.1%
 
0.233727797910.1%
 
-0.363598167910.1%
 
0.827008843410.1%
 
0.363596707610.1%
 
0.912739038510.1%
 
-0.124107338510.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.8100426210.1%
 
-1.80831384710.1%
 
-1.80277442910.1%
 
-1.80051755910.1%
 
-1.79689598110.1%
 
-1.79373526610.1%
 
-1.78180134310.1%
 
-1.77307629610.1%
 
-1.77226352710.1%
 
-1.76788294310.1%
 
ValueCountFrequency (%) 
1.70540058610.1%
 
1.70496332610.1%
 
1.70403146710.1%
 
1.70339608210.1%
 
1.70262515510.1%
 
1.70138442510.1%
 
1.6929168710.1%
 
1.68982768110.1%
 
1.68834495510.1%
 
1.68796014810.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.584496201800789e-09
Minimum-2.641596794128418
Maximum2.337723970413208
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:29:49.291797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.641596794
5-th percentile-1.901313818
Q1-0.7113724947
median0.2104151621
Q30.7663364261
95-th percentile1.382168519
Maximum2.33772397
Range4.979320765
Interquartile range (IQR)1.477708921

Descriptive statistics

Standard deviation0.9999999957
Coefficient of variation (CV)631115426.2
Kurtosis-0.5278779815
Mean1.584496202e-09
Median Absolute Deviation (MAD)0.643247366
Skewness-0.5443612763
Sum1.584496202e-06
Variance0.9999999913
2020-08-25T00:29:49.391697image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.10938727910.1%
 
1.4244033110.1%
 
-0.330435067410.1%
 
-0.0857819989310.1%
 
1.47990977810.1%
 
1.03765070410.1%
 
0.752658665210.1%
 
0.727264881110.1%
 
2.01061534910.1%
 
1.12644815410.1%
 
0.813199043310.1%
 
0.998743772510.1%
 
0.131033599410.1%
 
-0.468402534710.1%
 
-0.305034309610.1%
 
0.0165278427310.1%
 
-0.922531783610.1%
 
1.60293710210.1%
 
1.1146532310.1%
 
-1.04433286210.1%
 
0.856149077410.1%
 
-0.991510927710.1%
 
-1.55603814110.1%
 
1.54040706210.1%
 
0.520201802310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.64159679410.1%
 
-2.56104159410.1%
 
-2.54193186810.1%
 
-2.4808080210.1%
 
-2.4171862610.1%
 
-2.39566302310.1%
 
-2.37618970910.1%
 
-2.37398171410.1%
 
-2.36895680410.1%
 
-2.36568903910.1%
 
ValueCountFrequency (%) 
2.3377239710.1%
 
2.01061534910.1%
 
1.93172359510.1%
 
1.87555420410.1%
 
1.79535305510.1%
 
1.77411174810.1%
 
1.75217819210.1%
 
1.71415960810.1%
 
1.69717299910.1%
 
1.68586444910.1%
 

Interactions

2020-08-25T00:29:26.107644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:26.253011image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:26.404327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:26.555025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:26.706998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:26.860180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:27.015511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:27.167178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:27.321466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:27.474826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:27.632255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:27.780568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:27.938472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:28.102179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:28.266910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:28.434951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:28.798291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:28.961243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:29.125642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:29.293695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:29.473949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:29.642892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:29.803663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:29.966719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:30.130724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:30.295358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:30.459968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:30.622605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:30.786227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:30.949466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:31.111632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:31.276130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:31.448738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:31.605034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:31.764468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:31.935331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:32.102332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:32.266337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:32.432349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:32.596834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:32.760751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:32.937210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:33.111956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:33.276431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:33.434800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:33.789401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:33.953484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:34.116478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:34.283382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:34.447345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:34.611409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:34.776211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:34.941563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:35.107615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:35.273611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:35.431891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:35.587852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:35.755682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:35.920864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:36.084218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:36.247347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:36.413346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:36.578238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:36.742142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:36.910652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:37.074350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:37.228826image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:37.385324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:37.549624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:37.711699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:37.874705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:38.037585image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:38.212539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:38.374145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:38.702549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:38.865306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:39.030710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:39.182923image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:39.336258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:39.509764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:39.677049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:39.841994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:40.011119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:40.174708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:40.337381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:40.502242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:40.667568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:40.834138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:40.987995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:41.139188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:41.300256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:41.461790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:41.622311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:41.783703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:41.947042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:42.114917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:42.277556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:42.442364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:42.606165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:42.760350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:42.919126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:43.092173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:43.252928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:43.583138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:43.745471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:43.909522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:44.074008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:44.236422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:44.399388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:44.567299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:44.727141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:44.869622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:45.022783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:45.173186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:45.324485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:45.479052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:45.630388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:45.785498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:45.935909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:46.094781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:46.244333image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:29:49.509740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:29:49.740222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:29:49.960738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:29:50.183418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:29:46.493143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:29:46.759880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
0-1.505741-0.985161-1.624396-0.855436-1.432705-0.810323-0.6017290.0114890.440476-0.3259240.296129
12.2345481.7754661.6619590.5023290.784906-0.9476691.288117-1.384229-0.514454-1.6253090.031571
21.6794971.3363602.3628841.1241501.068917-1.159347-1.6031980.117568-1.0249120.7512980.831221
3-1.700708-1.331108-1.070706-0.7857460.151469-0.376526-1.1932240.003769-1.591838-0.4438370.027326
4-1.211680-0.480722-0.600695-1.577293-1.4902861.6529861.188035-0.8262500.8321481.220075-0.431557
50.4902770.2122291.5269040.9765860.011235-1.4504030.635190-1.0762531.5889431.703396-0.242575
6-0.689700-0.049872-0.2990290.475795-0.5021370.7182060.755151-0.0996970.7812560.9273921.157562
70.063466-0.610241-0.124463-0.2376780.9077020.876735-0.8577520.8671521.4317500.8391411.013263
8-0.613945-0.5469180.5313471.3538631.012377-1.022361-1.421458-0.255845-0.6455901.2453191.325784
9-0.653721-1.401781-0.8575151.3642470.360752-0.998315-1.6101840.7445911.344029-1.4836910.835077

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
9900.7075420.3813070.053708-0.276286-0.1205980.4081481.290591-0.2643370.6661511.701384-1.298751
9911.0706130.5172110.8582740.381372-0.8184721.2185511.413253-0.8983920.1564401.414984-1.874483
992-1.044941-0.473453-0.2461300.2667630.1849300.2431110.860831-1.313556-0.4635570.1879880.766103
993-0.220984-0.099360-1.922436-1.619388-0.0632250.650407-0.534441-1.369786-1.175908-1.0849481.231910
9940.9320561.4377770.198907-1.091872-0.150524-1.3394800.8327070.5686270.573897-1.388561-1.698412
9950.5587850.3947301.5876791.082513-1.384483-0.912167-1.659778-1.464664-1.737949-0.236080-0.863536
996-0.574210-0.073274-0.707206-1.6740360.986902-0.368112-0.3609331.6970050.3703671.2095610.725334
9970.9063081.7836821.7915480.997886-0.6710670.692669-0.037209-0.1012371.0029880.870134-0.140165
998-1.076314-0.6640400.1361590.6816100.586859-0.0751260.7181641.381397-1.5853820.7984970.671559
999-1.810947-1.592426-1.453785-0.5402920.6010971.298561-0.3431310.3725210.680955-1.2637310.520202